Short Course: Adaptive Clinical Trials

Transcription

1 Short Course: Adaptive Clinical Trials Presented at the 2 Annual Meeting of the Society for Clinical Trials Vancouver, Canada Roger J. Lewis, MD, PhD Department of Emergency Medicine Harbor-UCLA Medical Center David Geffen School of Medicine at UCLA Los Angeles Biomedical Research Institute Berry Consultants, LLC Financial Disclosures Berry Consultants, LLC Multiple clients U Support from National Institutes t of Health Food and Drug Administration AspenBio Pharma Cell>Point, LLC Octapharma USA Octapharma AG Outline The philosophy of adaptive clinical trials Planned change is good! Categories of adaptive trial designs Specific adaptive strategies Implementation/Logistics Data and Safety Monitoring Boards Acceptability to key stakeholders

2 Philosophy of Adaptive Trials Clarity of goals E.g., Proof of concept vs identification of dose to carry forward vs confirmation of benefit Frequent looks at the data and datadriven modification of the trial Adaptive by design Extensive use of simulation to fine tune key trial characteristics JAMA 26;296: Adaptation: Definition Making planned, well-defined changes in key clinical trial design parameters, during trial execution based on data from that trial, to achieve goals of validity, scientific efficiency, and safety Planned: Possible adaptations defined a priori Well-defined: Criteria for adapting defined Key parameters: Not minor inclusion or exclusion criteria, routine amendments, etc. Validity: Reliable statistical inference 2

3 The Adaptive Process Begin Data Collection with Initial Allocation and Sampling Rules Analyze Available Data Continue Data Collection Stopping Rule Met? Revise Allocation and Sampling Rules per Adaptive Algorithm Stop Trial or Begin Next Phase in Seamless Design Historical Context Historically, obtaining results that were reliable and valid required fixed study designs Allowed the determination of theoretical error rates Fundamental characteristic of the culture of biostatistics and clinical trial methodology Why are Study Designs Fixed? It s easiest to calculate type I error rates if the design parameters of the trial are all constant There are some other reasons: Results obtained using Standard approaches are generally considered valid Logistically simpler to execute Fixed designs are less sensitive to drift in the characteristics of subjects over time 3

4 Type of Adaptive Rules Allocation Rule: how subjects will be allocated to available arms Sampling Rule: how many subjects will be sampled at next stage Stopping Rule: when to stop the trial (for efficacy, harm, futility) Decision Rule: decision and interim decisions pertaining to design change not covered by the previous three rules Adapted from Vlad Dragalin Example Increasing dose Placebo Dose A Dose B Dose C N N 2 Rule: Drop a dose if rate of AE, AE2, or AE3 appears to be above the tolerable limit at either N or N 2 based on lower limit of model-based 8% CI: Limits: Pr(AE) <.2 Pr(AE2) <.2 Pr(AE3) <.4 Example Increasing dose Placebo Dose A Dose B Dose C N Pr(AE) <.2 Pr(AE2) <.2 Pr(AE3) <.4 N No dose meets criteria for early termination so all will be continued until N 2. 4

5 Example Increasing dose Placebo Dose A Dose B Dose C Pr(AE) <.2 N N 2 Pr(AE2) <.2 Pr(AE3) <.4 N N 2 Example Increasing dose Placebo Dose A Dose B Dose C Pr(AE) <.2 N N 2 Pr(AE2) <.2 Pr(AE3) <.4 N N 2 Example Simulations, conducted under a wide range of assumptions regarding the rates of AE, AE2, and AE3, used to verify: Ability of design to reliably terminate poorly tolerated arms Ability of design to reliably retain welltolerated arms Learn phase (phase II dose finding) study Control of type I error rate for efficacy based on taking 2 active arms forward 5

6 When is Adaptation Most Valuable? Outcomes or biomarkers available rapidly relative to time required for entire trial Substantial morbidity, risks, costs Large uncertainty t regarding relative efficacy, adverse event rates, etc. Logistically practical Able to secure buy-in of stakeholders Why Not Adapt? Determining traditional type I and type II error rates is more difficult Usually need to use simulation Statistical training issues Most statisticians have never designed or analyzed an adaptive trial Logistical Issues Data availability Centralized randomization Drug supply 6

7 Categories of Adaptive Trials Can be classified based on adaptive component(s) Allocation rule Sampling rule Sample size re-estimation Stopping rule Group sequential trial Decision rule Seamless phase II/III Goal and place in drug development Learn versus confirm Proof of concept, dose finding, seamless phase II/III Response-adaptive dose finding Categories of Adaptive Trials Information driving adaptation Adaptive Covariates Variance Sample size re-estimation Response adaptive Primary endpoint Biomarker Safety outcomes Response-adaptive dose finding Some (Bayesian) Adaptive Strategies Frequent interim analyses Explicit longitudinal modeling of the relationship between proximate endpoints and the primary endpoint of the trial Response-adaptive randomization to efficiently address one or more trial goals Explicit decision rules based on predictive probabilities at each interim analysis Dose-response modeling Extensive simulations of trial performance 7

8 Frequent Interim Analyses Frequent interim analyses based on Markovchain Monte Carlo (MCMC) estimates of Bayesian posterior probability distributions, with multiple imputation and estimation of unknown trial parameters and patient outcomes. Typically quantify Evidence of treatment efficacy Trial futility/predictive probability of success Safety and rates of adverse events Longitudinal Modeling Explicit longitudinal modeling of the relationship between proximate endpoints and the primary (generally longer term) endpoint of the trial to better inform interim decision making, based on the data accumulating within the trial and without assuming any particular relationship at the beginning of the trial. Used to learn about, and utilize, the relationship between proximate and final endpoints Frequently misunderstood as making assumptions or using biomarkers Response-adaptive Randomization Response-adaptive randomization to improve important trial characteristics May be used to address one or more of: To improve subject outcomes by preferentially randomizing patients to the better performing arm To improve the efficiency of estimation by preferentially assigning patients to doses in a manner that increases statistical efficiency To improve the efficiency in addressing multiple hypotheses by randomizing patients in a way that emphasizes sequential goals Includes arm dropping 8

9 Decision Rules/Predictive Probabilities Explicit decision rules based on predictive probabilities at each interim analysis to define when to stop for futility, early success, etc. Examples May define success or futility based on the predictive probability of success if trial is stopped and all patients followed to completion May define success or futility based on the predictive probability of success of a subsequent phase III trial May combine probabilities logically: probability that the active agent is both superior to a control arm and non-inferior to an active comparator Design transitions : e.g., phase II to phase III Dose-response Modeling Dose-response modeling, when applicable, so that information from all patients informs the estimate of the treatment effect at all doses this improves the reliability of interim decision making and improves accuracy in the updating of interim randomization proportions. Examples Logistic dose-response model: assumes monotonicity Normal dynamic linear model (NDLM): borrows information from adjacent doses but doesn t assume a particular shape of the relationship Extensive Simulations Extensive simulations of trial performance to ensure that the type I error rate, power and accuracy in estimation of treatment effect(s), the rates of adverse events, or dose finding are well defined and acceptable, across a very wide range of possible true treatment effect sizes, dose-response relationships, and population characteristics. Often end up exploring and understanding the performance characteristics across a range of null hypotheses much broader than with traditional approaches 9

10 The Adaptive Process Begin Data Collection with Initial Allocation and Sampling Rules Analyze Available Data Continue Data Collection Stopping Rule Met? Revise Allocation and Sampling Rules per Adaptive Algorithm Stop Trial or Begin Next Phase in Seamless Design Components of an Adaptive Trial Adaptive Machinery Logistics Drug Supply Randomization System CRO/Data Clinical Site Site 2 Site n Components of an Adaptive Trial Adaptive Machinery Logistics Drug Supply Randomization System CRO/Data Clinical Site Site 2 Site n

11 Components of an Adaptive Trial Adaptive Machinery Logistics Drug Supply Randomization System CRO/Data Clinical Site Site 2 Site n Components of an Adaptive Trial Adaptive Machinery Adaptive Data Algorithm Analysis Logistics Drug Supply Randomization System CRO/Data Clinical Site Site 2 Site n Components of an Adaptive Trial Sponsor Steering Committee Independent DSMB Adaptive Machinery Adaptive Data Algorithm Analysis Logistics Drug Supply Randomization System CRO/Data Clinical Site Site 2 Site n

12 Components of an Adaptive Trial Sponsor Steering Committee Independent DSMB Adaptive Machinery Adaptive Data Algorithm Analysis Logistics Drug Supply Randomization System CRO/Data Clinical Site Site 2 Site n Components of an Adaptive Trial Sponsor Steering Committee Independent DSMB Adaptive Machinery Adaptive Data Algorithm Analysis Logistics Drug Supply Randomization System CRO/Data Clinical Site Site 2 Site n Data and Safety Monitoring Boards Purpose To ensure continued safety, validity, feasibility, and integrity of the clinical trial To ensure the trial is conducted according to a priori i plan, including adaptation ti Structure Learn phase: usually includes internal personnel Confirm phase: generally includes only independent, external members 2

13 Data and Safety Monitoring Boards What s different in an adaptive trial? Requires expertise to assess whether the planned adaptations continue to be safe and appropriate May increase need to include sponsor personnel What s unchanged in an adaptive trial? The DSMB ensures completion of the trial as planned, including the adaptation It is the trial that s adaptive, not the DSMB IRB Review IRBs review/approve the full protocol, including the planned adaptations No new review when adaptations made IRBs may request to be informed (e.g., new sample size, dropping of a surgical arm) Amendments are different Not preplanned Irony Little changes (e.g., amendments) may require IRB review Big changes (adaptations) are defined by design and only reviewed/approved once Acceptability to Key Stakeholders FDA FDA Critical Path Initiative 2 Guidance for the Use of Bayesian Statistics in Medical Device Trials 2 Draft Guidance for Adaptive Design Clinical Trials for Drugs and Biologics Joint Regulatory Science initiative with NIH Multiple adaptive trials accepted in development plans PhRMA Highly active working group on adaptive trials DIA 26 PhRMA/FDA Conference on Adaptive trials Many adaptive trials designed or initiated in industry Peer reviewers may be unfamiliar with adaptive design principles 3

14 FDA Guidance Documents The ADAPT-IT Project Supported by an NIH U grant with funds from both NIH and FDA Redesigning four clinical trials for treatments of neurological emergencies control of blood sugar in stroke hypothermia for spinal cord injury with paralysis treatment of prolonged seizures hypothermia after cardiac arrest Work closely with project teams and statisticians to create more efficient, ethical version of proposed trials Online Tools and Resources MD Anderson Lots of good utilities, including Adaptive Randomization to help with response adaptive trials Allows arms; minimum number of patients before adapting randomization scheme; maximum number of patients or length of trial Free Commercial resources 4

15 Conclusions Not all trials need (or should have) adaptive designs When used appropriately, adaptive designs may: Improve efficiency i and reduce cost Maximize the information obtained Minimize risk to subjects and sponsor An adaptive design will not save a poorly planned trial or make a treatment effective 5